References and Related Work

Kindred Projects

The following projects and initiatives use satellite imagery, oftentimes in combination with various forms of computer vision, to track phenomena of interest in the built and natural world. Until recently, much of this work has been tuned for specific geospatial problems.

Technical Bibliography

The following research projects and open-source codebases are specifically concerned with the application of 'deep-learning' techniques to satellite imagery. Several of these projects also employ OpenStreetMap labels as training data, as we do.

Humanitarian Mapping with Deep Learning by Stanford graduate student, Lars Roemheld (2016). Like the Terrapattern project, this project uses OpenStreetMap (OSM) to help train a neural net, in order to help support map creation in the developing world.

OSM-Crosswalk-Detection by Marcel Huber (2015). Developed at the University of Applied Sciences Rapperswil, this project again trains deep learning models with OSM labels to locate Swiss crosswalks.

skynet-data (2016), by Anand Thakker at DevelopmentSEED, is "a pipeline to simplify building a set of training data for aerial-imagery-based and OpenStreetMap-based machine learning." Skynet uses OSM QA Tiles to generate ground truth images where each color represents some category derived from OSM features.